In recent years, we have seen the superior performance of graph convolutional networks [1]}, [2]}, [3]} on several machine learning tasks on graphs. Relational GCN [4]} extends GCN to graph with different edge types by proposing a learnable weight matrix for each relation to improve link prediction performance. Neural Graph Collaborative Filtering (NGCF) [5]} operates on user-item bipartite graph and explicitly incorporates the high-order connectivity between users and items to learn their embeddings. LightGCN [6]} simplifies GCN for recommendation by proposing linear embedding propagation between user and items.
